no-op

PiperOrigin-RevId: 272678863

Author: caveness

tensorflow_data_validation/statistics/generators/image_stats_generator_test.py

@@ -327,7 +327,7 @@ def test_image_stats_generator_disable_size_stats(self):
 
 def _read_file(filepath):
   """Helper method for reading a file in binary mode."""
-  f = tf.gfile.Open(filepath, mode='rb')
+  f = tf.io.gfile.GFile(filepath, mode='rb')
   return f.read()
 
 

tensorflow_data_validation/utils/stats_gen_lib.py

@@ -87,8 +87,8 @@ def generate_statistics_from_tfrecord(
   if output_path is None:
     output_path = os.path.join(tempfile.mkdtemp(), 'data_stats.tfrecord')
   output_dir_path = os.path.dirname(output_path)
-  if not tf.gfile.Exists(output_dir_path):
-    tf.gfile.MakeDirs(output_dir_path)
+  if not tf.io.gfile.exists(output_dir_path):
+    tf.io.gfile.makedirs(output_dir_path)
 
   batch_size = (
       stats_options.desired_batch_size if stats_options.desired_batch_size
@@ -162,8 +162,8 @@ def generate_statistics_from_csv(
   if output_path is None:
     output_path = os.path.join(tempfile.mkdtemp(), 'data_stats.tfrecord')
   output_dir_path = os.path.dirname(output_path)
-  if not tf.gfile.Exists(output_dir_path):
-    tf.gfile.MakeDirs(output_dir_path)
+  if not tf.io.gfile.exists(output_dir_path):
+    tf.io.gfile.makedirs(output_dir_path)
 
   batch_size = (
       stats_options.desired_batch_size if stats_options.desired_batch_size
@@ -318,13 +318,13 @@ def get_csv_header(data_location: Text,
     ValueError: If any of the input files is not found or empty, or if the files
       have different headers.
   """
-  matched_files = tf.gfile.Glob(data_location)
+  matched_files = tf.io.gfile.glob(data_location)
   if not matched_files:
     raise ValueError(
         'No file found in the input data location: %s' % data_location)
 
   # Read the header line in the first file.
-  with tf.gfile.GFile(matched_files[0], 'r') as reader:
+  with tf.io.gfile.GFile(matched_files[0], 'r') as reader:
     try:
       result = next(csv.reader(reader, delimiter=delimiter))
     except StopIteration:
@@ -333,7 +333,7 @@ def get_csv_header(data_location: Text,
 
   # Make sure that all files have the same header.
   for filename in matched_files[1:]:
-    with tf.gfile.GFile(filename, 'r') as reader:
+    with tf.io.gfile.GFile(filename, 'r') as reader:
       try:
         if next(csv.reader(reader, delimiter=delimiter)) != result:
           raise ValueError('Files have different headers.')
@@ -354,7 +354,7 @@ def load_statistics(
   Returns:
     A DatasetFeatureStatisticsList proto.
   """
-  serialized_stats = next(tf.python_io.tf_record_iterator(input_path))
+  serialized_stats = next(tf.compat.v1.io.tf_record_iterator(input_path))
   result = statistics_pb2.DatasetFeatureStatisticsList()
   result.ParseFromString(serialized_stats)
   return result

tensorflow_data_validation/utils/stats_gen_lib_test.py

@@ -81,7 +81,7 @@ def _make_example(self, feature_name_to_type_values_tuple_map):
 
   def _write_tfexamples_to_tfrecords(self, examples, compression_type):
     data_location = os.path.join(self._get_temp_dir(), 'input_data.tfrecord')
-    with tf.python_io.TFRecordWriter(
+    with tf.io.TFRecordWriter(
         data_location, options=compression_type) as writer:
       for example in examples:
         writer.write(example.SerializeToString())
@@ -115,8 +115,11 @@ def test_stats_gen_with_tfrecords_of_tfexamples(self, compression_type):
         })
     ]
     tf_compression_lookup = {
-        CompressionTypes.AUTO: tf.io.TFRecordCompressionType.NONE,
-        CompressionTypes.GZIP: tf.io.TFRecordCompressionType.GZIP}
+        CompressionTypes.AUTO:
+            tf.compat.v1.python_io.TFRecordCompressionType.NONE,
+        CompressionTypes.GZIP:
+            tf.compat.v1.python_io.TFRecordCompressionType.GZIP
+    }
     input_data_path = self._write_tfexamples_to_tfrecords(
         examples, tf_compression_lookup[compression_type])
 

tensorflow_data_validation/utils/validation_lib.py

@@ -83,8 +83,8 @@ def validate_examples_in_tfrecord(
   if output_path is None:
     output_path = os.path.join(tempfile.mkdtemp(), 'anomaly_stats.tfrecord')
   output_dir_path = os.path.dirname(output_path)
-  if not tf.gfile.Exists(output_dir_path):
-    tf.gfile.MakeDirs(output_dir_path)
+  if not tf.io.gfile.exists(output_dir_path):
+    tf.io.gfile.makedirs(output_dir_path)
 
   with beam.Pipeline(options=pipeline_options) as p:
     _ = (
@@ -161,8 +161,8 @@ def validate_examples_in_csv(
   if output_path is None:
     output_path = os.path.join(tempfile.mkdtemp(), 'anomaly_stats.tfrecord')
   output_dir_path = os.path.dirname(output_path)
-  if not tf.gfile.Exists(output_dir_path):
-    tf.gfile.MakeDirs(output_dir_path)
+  if not tf.io.gfile.exists(output_dir_path):
+    tf.io.gfile.makedirs(output_dir_path)
 
   # If a header is not provided, assume the first line in a file
   # to be the header.

tensorflow_data_validation/utils/validation_lib_test.py

@@ -89,7 +89,7 @@ def test_validate_examples_in_tfrecord(self):
 
     temp_dir_path = self.create_tempdir().full_path
     input_data_path = os.path.join(temp_dir_path, 'input_data.tfrecord')
-    with tf.python_io.TFRecordWriter(input_data_path) as writer:
+    with tf.io.TFRecordWriter(input_data_path) as writer:
       for example in input_examples:
         example = text_format.Parse(example, tf.train.Example())
         writer.write(example.SerializeToString())